题  目：Modeling the interatomic potential by deep learning

时  间：2019年11月13日（周三）10:00

地  点：交通大楼604会议室

报告人：王涵 研究员 （北京应用物理和计算数学研究所）

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                   土木与交通学院

                  2019年11月11日

报告人简介：

王涵，北京应用物理与计算数学研究所研究员。2006年获北京大学计算数学专业本科学位，2011年获北京大学计算数学博士学位。2007-2008，德国马克斯普朗克研究所访问学习。2011-2014年在德国自由大学（Freie Universität Berlin）从事博士后研究。2014-2018年在中国工程物理研究院高性能数值模拟软件中心担任研究科学家。2018年至今在北京应用物理与计算数学研究所担任研究科学家。 

王涵研究员长期从事分子动力学的数值分析和快速算法研究，以及多尺度模型和模拟研究，发表学术论文30余篇。王涵研究员及其团队研发的基于深度学习的分子动力学原子间相互作用势建模项目在专业领域内受到广泛的关注和认可，并成功发布了开源软件DeePMD-kit，对该领域产生了重要的影响。

报告摘要：

     In silico design of molecules requires an accurate description of the interatomic potential. In the context of molecular simulation, one usually faces the dilemma that the first principle potential energies are accurate but computationally expensive, while the empirical force fields are efficient but of limited accuracy. In this talk, we try to solve this dilemma by using recently developed deep learning and active learning algorithms. We discuss the topic in two aspects: model construction and data generation. In terms of model construction, we introduce the Deep Potential scheme based on a many-body potential and interatomic forces generated by a carefully crafted deep neural network trained with first principle data. We show that the proposed scheme provides an efficient and accurate protocol for a variety of systems, including bulk materials and molecules, and, in particular, for some challenging systems like a high-entropy alloy system. In terms of data generation, we present a new active learning approach named Deep Potential Generator (DP-GEN), which is an iterative procedure including exploration, labeling, and training steps. By the example system of Al-Mg alloys, we demonstrate that DP-GEN can generate uniformly accurate potential energy models with a minimum number of labeled data.